计算机与现代化

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异构边缘网络中内容扩散策略及优化

  

  1. (1.中国科学院声学研究所国家网络新媒体工程技术研究中心,北京100190;2.中国科学院大学,北京100190)
  • 收稿日期:2019-05-17 出版日期:2020-02-13 发布日期:2020-02-13
  • 作者简介:薛寒星(1992-),男,山西运城人,博士研究生,研究方向:边缘网络,内容扩散,E-mail: xuehx@dsp.ac.cn; 尤佳莉(1982-),女,副研究员,博士,研究方向:未来网络,内容分发网络,E-mail: youjl@dsp.ac.cn; 王劲林(1964-),男,研究员,研究方向:无线通信,未来网络,E-mail: wangjl@dsp.ac.cn。
  • 基金资助:
    中国科学院战略性科技先导基金资助项目(XDC02010701)

Design and Optimization of Content Diffusion Strategies in Heterogeneous Edge Network

  1. (1. National Network New Media Engineering Technology Research Center, Institute of Acoustics, Chinese Academy of Sciences,
    Beijing 100190, China;2. University of Chinese Academy of Sciences, Beijing 100190, China)
  • Received:2019-05-17 Online:2020-02-13 Published:2020-02-13

摘要: 通过内容扩散系统在边缘网络中部署热门内容的副本可以有效缓解骨干网络的负载压力。因为网络边缘设备的能力和其所属用户的兴趣可能存在差异,内容扩散策略需要充分考虑边缘网络的异构性。本文首先提出一种分层的边缘网络拓扑和去中心化的内容扩散策略实现热门内容副本的扩散。基于设备的能力、负载和兴趣特征,本文进一步优化内容扩散策略,包括:基于兴趣的扩散目标调整策略、启发式的缓存节点选择策略、覆盖率感知的传输内容选择和传输负载感知的接收节点选择策略。实验结果表明优化后的扩散策略在边缘网络中拥有更稳定的高服务成功率,提高了约25.5%的本地获取服务的概率,减少了约7.6%的完成扩散所需时间。

关键词: 异构边缘网络, 内容扩散, 去中心化, 自适应调整, 传输策略

Abstract: Deploying the replications of popular content in the edge network through the content diffusion system can effectively alleviate the load on the backbone network. Because the capacities of terminals and the interests of their users may differ, the content diffusion strategies need to fully consider the heterogeneous. This paper proposes a layered topology of network and a decentralized content diffusion strategy to deploy the replications. Based on the capabilities, load and interests of terminals, this paper further optimizes the diffusion of content. The optimization strategy includes the adjustment of target of diffusion based on the interest features, the heuristic caching node selection strategy, the coverage rate-aware selection of transmission content and transmission load-aware selection of receiving node. Experimental results show that the optimized diffusion system can provide a more stable success rate of services, improve the probability of local services by about 25.5%, and reduce the time to complete the diffusion of content by about 7.6%.

Key words: heterogeneous edge network, content diffusion, decentralized, adaptive adjustment, transmission strategies

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